1. Field
The following description relates to a method of classifying and an apparatus to classify an input pattern.
2. Description of Related Art
Artificial neural networks have been applied to various fields in which pattern recognition is performed, such as, for example, pattern classification, continuous mapping, nonlinear system identification, nonlinear control, robot control, and other fields known to one of ordinary skill in the art. The artificial neural network is obtained by engineering a cell structure model of the human brain where a process of efficiently recognizing a pattern is performed. An artificial neural network refers to a calculation model that is based on software or hardware designed to imitate biological calculation ability by applying many artificial neurons interconnected through connection lines.
The human brain consists of neurons that are basic units of a nerve and encrypts or decrypts information according to types of dense connections between the neurons. The artificial neutrons are obtained through the simplification of biological neuron functionality. In addition, the artificial neural network performs a cognition process or a learning process by interconnecting artificial neurons having connection intensities. The connection intensity, which is also referred to as a connection weight, is a predetermined value of the connection line.
Pattern classification methods are applied to medical devices. Electrical biological signals of patients that are received by medical devices, such as, for example, electrocardiography signals, brain waves, electromyography signals, and other signals of patients known to one of ordinary skill in the art, are measured and patterns of the measured biological signals are classified to determine diseases. Recently, research has been directed to ways by which a human method of pattern recognition may be applied to a practical computer, including an application of an artificial neural network to a practical computer that has been obtained based on an engineered model of a cell structure of the human brain that performs pattern recognition.
In order to model a human ability to recognize patterns, an artificial neural network has been designed to perform mapping between input patterns and output patterns based on an algorithm generated by the artificial neural network, thereby enabling the artificial neural network to imitate human learning ability. In addition, based on learning results, the artificial neural network has been designed to generalize by generating a relatively correct output based on an input pattern not reflecting an imitation of human learning ability.